Jin, Zhihao2026-04-202026-04-202026-04-20http://hdl.handle.net/10393/51551https://doi.org/10.20381/ruor-31871As Earth observation missions continue to expand in both scale and complexity, optimization of satellite altimetry data acquisition has become a critical challenge. This thesis addresses continuing needs for scheduling hydrology-related measurements, such as those of oceans, lakes, and glaciers, efficiently while operating under strict constraints, including limited onboard memory, command execution limits, and overall satellite operability. To this end, we propose a constraint-aware heuristic that dynamically prioritizes and schedules altimetry targets, accounting for variations in spatial resolution and scientific priority. The proposed methodology formulates mission planning as a multi-constraint optimization problem that incorporates satellite position and altitude, and to our knowledge, this is the first work to introduce such an approach. It adopts a knapsack-like formulation, accounting for variable data rates, target durations, and orbital geometry. The algorithm includes utility functions for memory management and spatially-aware target merging and supports dynamic mode downgrading to optimize data acquisition under resource limitations. A custom objective function is introduced to evaluate scheduling effectiveness, considering both data quality and target priority. Experimental results, based on real and synthetic altimetry data, demonstrate the approach's scalability (handling up to 3,000 targets in minutes), high memory utilization efficiency, and superior performance compared to conventional optimization methods such as the Jaya algorithm. The algorithm delivers explainable, near-optimal target schedules with linear time complexity, making it a strong candidate for onboard autonomous planning.enArtificial IntelligenceAltimetry Data AcquisitionHeuristic OptimizationSatellite SchedulingMission PlanningResource AllocationThesis